Lightweight cascade framework for optic disc segmentation

The accurate segmentation of the optic disc (OD) is important in diagnosing and evaluating many retinal diseases. However, the OD boundary is unclear, making the task of automatic OD segmentation very challenging. Recently,many researchers have applied convolutional neural network (CNN) technology to the automatic segmentation of OD, and the network has been widened and deepened. It can effectively improves the accuracy of segmentation but also requires high computational complexity and large memory consumption. To overcome the above defects, we propose a segmentation framework of a lightweight cascade CNN. It consists of a designed‐to‐be‐lightweight segmentation network and a shape‐refinement network cascade, cascading a shape‐refined network behind a segmentation network to compensate for the degraded performance of the segmentation network after lightweight design. We tested our framework on three databases, DRIVE, DIARETDB1, and DRIONS‐DB, and found that its segmentation performance is slightly better than that of u‐net, and the trainable parameters are approximately 1/35 that of u‐net. After verified by the DRIVE dataset, the memory used for training and testing is only about 1/3 of u‐net. The method proposed in this paper can greatly reduce trainable parameters and computational resource consumption while guaranteeing satisfactory segmentation performance of the model.